{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cca6eb43-a48b-4046-8e42-54b9b6b4a038",
   "metadata": {},
   "source": [
    "# 4.回归分析\n",
    "\n",
    "- 二维可视化\n",
    "- 三维可视化\n",
    "- 一元线性回归\n",
    "- 多元线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a79ef54-63e6-429f-ba9f-590b736a7f29",
   "metadata": {},
   "source": [
    "# 4.1 在二维空间中绘制散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90a18b9-d8e8-431c-856c-35f1e4ed05e1",
   "metadata": {},
   "source": [
    "### 1.任务描述\n",
    "\n",
    "一组房屋面积的数据如下（16个）:\n",
    "\n",
    "137.97,104.50,100.00,124.32,79.20,99.00,124.00,114.00,106.69,138.05,53.75,46.91,68.00,63.02,81.26,86.21。\n",
    "\n",
    "与房屋面积对应的房屋价格数据如下（16个）:\n",
    "\n",
    "145.00,110.00,93.00,116.00,65.32,104.00,118.00,91.00,62.00,133.00,51.00,45.00,78.50,69.65,75.69,95.30\n",
    "\n",
    "要求：\n",
    "\n",
    "- 以房屋面积为x轴，以房屋价格为y轴，绘制散点图\n",
    "- x轴名称设置为面积（平方米），字体大小设置为14\n",
    "- y轴名称设置为价格（万元），字体大小设置为14\n",
    "- x轴刻度范围设置为(40,150)\n",
    "- y轴刻度范围设置为(40,150)\n",
    "- 图形标题设置为：商品房销售价格评估系统，字体大小设置为20"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8d7baa9c-93a2-42f3-a3c1-231cdb587f2d",
   "metadata": {},
   "source": [
    "### 2.知识准备\n",
    "\n",
    "见教程。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ad989a-9b82-43e1-b841-e74284cd5936",
   "metadata": {},
   "source": [
    "### 3.任务分析\n",
    "\n",
    "在进行回归分析前，需要把数据集的分布情况通过matplotlib.pyplot.scatter方法描绘出来，同时观察数据集的分布是否满足线性回归分析的条件。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c6090-cfda-4f46-a550-22a368e41e4a",
   "metadata": {},
   "source": [
    "### 4.任务实施\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec75eb6c-5da3-467d-a471-ca3b47242dd6",
   "metadata": {},
   "source": [
    "执行代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ae9da58-e339-4d22-9f8d-ca255711d89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a/cSokqngQIeqkOPHj5v1l7G/v79J5y2v4a1ubm5mu7YXX3xR7znu3Lkjpk6dKgICAkRoaKho2LChqFevnvD39xc+Pj5i1apVUt7ffvutROecO3eu1rmuXr0qPD09BQDRp08fsXnzZgFYto9GYmKicHV1lcpUuEYjJydHDBgwQADqJpXDhw8bPM6DBw9E+/btpeM89dRT4s8//zSYf8GCBVLewn00Tp06JVq1aiU+/vhjKU3TFPfWW2+J06dPC0dHR2FjYyOOHj1q8PiaGomir40bN+rk1dSOFO6nU7RGIyEhQWp2NHSvDx8+LA1V1bxMmfvDHH00Vq5cafLnbujQocU2cxW2bt06o8fr3r27zntiYmKETCYTNWvW1Bo2n5+fL2bMmCHatm0rsrKySn29ZFms0SCTaUYhODg4YPz48aU+zu3bt7F161ajw0MLO3XqVKnPVRKaGo34+HgEBwfrzXPlyhX8/PPPuHTpEv73v/+V+BwBAQFwcnJCYmKi3v2FR+BoalUee+wxjBs3zuAxN2zYgNOnT+uMEvHy8kLz5s2RnJyMjRs34pdffgEAXL58GY0aNdJ7rFu3bgEAZs6cic8++0xvnhYtWugdAllQUIDBgwcjMzMTbdq0wYkTJ6R9Fy9exJAhQ3DhwgU4OTlhz549eOaZZwxek5eXF/bv348RI0Zgy5YtOHr0KHr06IGzZ89q1bRo7Nq1S/p7VlYWxo4di4ULF+LJJ5/E8ePHtX7xr1mzBmfOnMGaNWuwefNm5ObmYvbs2QgPDzdYnh9//BEODg5wdHSEXC7Hs88+i7/++kvvyKbBgwfjww8/xHfffYdly5bp/Zx/+eWXUCqV8Pb21jvLpUKhwKhRoyCEQJ8+fbB7926DZbOE/v37w83NDfXq1UNAQAC8vb3h4OCANWvWYPr06cjOzkbDhg3x5ZdfokOHDgDUQ9o/+OADhISE4N133zV47JdffhnTpk2DQqFAw4YNUa9ePWRmZmLv3r1wdnbGqlWrtPI/fPgQQ4cOBQBs3LhRGu4OAFevXsXSpUuRnZ2N119/HVu3brXA3aAyq+hIh6qOq1evCgDCw8NDZ59SqdRpZy4oKBAbNmzQ6XSmmUGxadOmlixuiWnawo310fjpp5+kX17G+g8Yk5mZKXJycsS2bdukX4TZ2dkiNTVVZGZmSvlOnDghAIgOHToYPd6LL74oAIiVK1fq7EtLSxMJCQlCCCHVaJT1ZWjCqMmTJwtAPfHSX3/9pVWjERkZKQB1B9iYmBjx66+/ivfee0/vDKfbt28XL774ovjkk0+kcvv4+Ihdu3bpPa/mc6l5aTolLliwwOA900zuBUB4eXlJ98hUmhqKH374QWffuXPnRMuWLcX+/ft18mtqNNauXStCQ0O1OsIWNnHiROlabt68We41GkVdvHhRqmFydHQUc+bM0emHoekLA0C8+eabUgdPfe7evavV50Vz7KKjah49eiTatWsnAPVoIX22bNkinXfZsmVluEqyFAYaZLL4+HiDgcaECRMEAPHcc89JX4ajRo0SAET9+vW1euFrAg1TZkMsT6aOOtGMBHn33Xf17lcqlSI3N1c8fPhQPHr0yOBxNCMdhg4dqnf/yZMnSxRoFB06WdTXX39dpqaTvLw8kZ6errea/P79+8Lf318AEOPGjRMZGRlagUZubq4YPny4dG+XL18uAIhevXrpHGv+/PkCgOjZs6eUlp6ebrDMH3zwgQAgNX2NGzdOODg4CE9PT52ZRi9fvix69uwpAHXTmqaZw9XVVUybNk3cvn3b4HkK0wQOe/bs0bu/6MgVfZ1B8/Ly9H4+8vLyRN26dQUAsWbNGpGfn19hgUZCQoJ4++23pWaeLl26GB2mvmbNGmFrayv92+obcVSUJlBo1qyZ1n3bv3+/9Jlq0qSJuHnzpvjnn3/EiRMnxN69e8XGjRvF/Pnzxbvvviu8vLwEAGFvby9OnTpV5usm82KgQSbTF2golUoxbtw4AUDUrl1bHDlyRNp37949aX0QQN1rPSsrq9hAo0uXLib9stasixAbG2vyr3HNfBRFqVQqs/zaL/p6+eWXDd7P4gKNM2fOSA/BVq1aGXxpvmSLCzQ0Q04t1UcjNTVVDBo0SGRnZ+sEGkVpRozoG420bNkyARQ/ukUIIbKysoSvr6+ws7OT5nlYtGiRGDVqlJDJZGLHjh1CCPWcHa+99pr0wAwPDxeXL18WeXl5YsaMGdLDUS6Xi65du4o1a9aI2NhYg7/KiwYaBQUFIiUlRTx69Ejk5ubqjFAxNI+GSqUS+fn5IjMzU+uzGR0dLfr27SuEEAYDjRUrVugMXy1ueGtxa9hkZWWJgwcPimXLlomuXbtKx6pTp47OtPcZGRkiOTlZXLt2TZw7d04cPXpU/P7779K/AwDRrl07o8H2w4cPRa1atfT2kbly5Uqp/s81btzYpACHyg/7aFCpPXz4EK+++ir27t2LunXr4uDBg6hXr56039fXF19//TX69++PESNGYPXq1Th+/Djmzp1r9LiaqZPt7Oz0zpqYl5cHIQR8fHwAQKtvgqHZQDV9H4r2Y9BIS0uT/q5pcy7s4sWLSE1NRXBwMOrWrWu0/JplxLOzs+Hn56e1b8mSJZg/fz6cnZ2lMkVFReHQoUPSrJtjx47FnDlzjJ6jNDQjCG7fvo1XXnlFb57r168DANavX68zc6goNGohNzcXHTp0wNSpU6X9Pj4++P777wH8N1KouLIUvT+AehpsQP8sqEV99dVXSE1NxWuvvaZ1rKlTp6JVq1b4+++/8eGHH0pT3IeGhmL27Nl46aWXpH4bn3zyCYYOHYoPPvgAO3bswL59+7Bv3z40atQIZ86cMWk0Q3JyMoKCgorNN3r0aIwePdrg/pycHDg6OqJDhw7FTsednp4u/Xvpo2/22uJmy01NTdU7i6uTkxOmT5+OsWPHIiMjA1lZWdJyA8YcPnwYERER+O2331CzZk2d/e+88w7+/fdfjB49WqePTIMGDRAcHIybN2/CwcFBmtU1IiIC4eHh2LdvH06fPo0pU6Zg0qRJOHv2LLp3744mTZogNze3VFOwk2Uw0KBSycvLQ7du3fD333+jTp06qFOnjtE1GJYuXYq1a9dixYoVePTokdFjax40UVFR6NWrl87+pk2b4uLFi9LU5Jr8devWxc2bN/Ue09XVFVlZWVLeou7fvw9A3QlR39TcPXr0wC+//ILx48cb7ZhZnIKCAuTk5EAmk6GgoEArTRNoFB0a26pVK71l0hg8eLD0gC/u3IA6QNy8ebPRvMeOHTO63DoA1KhRo9hzGqIZslm4Y5+GJgAo7gGfm5uLzz77DDKZDFOnTsX27dulffXq1YOvry/CwsJw9epVdOzYEWPGjEG/fv30Bq/169fH999/j2vXrmH58uXYtGkT5syZY/K6HTKZDC4uLtJ02Q4ODlodUBMTE5Gbm4saNWpoDWtWqVRQKpXIy8tDVlaW1nuKO7cmEOvfvz927NhhNO+PP/6Ifv36FTv0tE6dOggLC8Pff/8NDw8P+Pr6SlOROzs7w9HREXZ2djh//jyuXLmC5s2b46mnnoK7uztcXV3h5uYm5b19+zZmzJiBs2fPonfv3jhy5IjW0gXvv/8+Nm/eDDs7OzRt2hSffvopEhIScO3aNVy9ehV79+7Frl27ULNmTdSuXRudO3fGgQMHMG7cOLzwwgtITk7G6dOn4e/vj1q1aqFbt244dOiQ3h8KVLEYaFCp2NvbY9++fZg9ezZmzJhR7EOnZ8+eiImJAQCjD02g+AeMhuaLuCTj5w3l1fz6CwwM1LtfExzpezCWxNSpU6VagB07dmDgwIEYNGgQNm7cWKbjmkKz7kzz5s1x9uzZEr9foVAgIyNDepV2rhHgvxokfb9yTbVkyRLcunULkZGRePzxx7UCDQBwd3fHjh074Obmho0bN5q0Tg6gXjAvMTGxRL+IAwICjNbiaBZV08w1YQ6m1PiU5j0//fSTFCwYMn78eFy5cgUvvPCCzholhbm7u2P37t3YsGGD1v+9zz77DEuXLgWgrt0aM2aM1vvq1auHxo0ba6VpAmVjtTIMMionBhpUap6entKXhb29vdSkUZjmC7YkX9qmLDIF/Bc0mJq/8HuK0qyMaahZ5OrVqwDUi0598MEHxZ7n448/xpAhQ0wuV3kYPXo0hg0bVuqJjRwcHODg4KCz0mxpaCYeK+2xkpKSMH/+fMhkMqM1aZoFzzRBUc2aNdGlSxe9eU+fPo1Lly7BxcXF5KHXhd/76NEjg4vHWUJxtROlfY8m+EtNTUVCQgJatmxp8vGPHDkCLy8vKUh4++23MXr0aJ0F0YYMGYKZM2eiYcOGaNWqFRo2bIiGDRsiODgYwcHBen+43L17FwDg7+9vcnmocmCgQWZha2trdCbOkqykauqqk5qgpjSrVBZ1+vRpANA7l8K1a9eQmpoKQN0Wbwpvb+8yl0njjz/+MMs19uzZEwcOHCjzcY4fP250lVhTaPpM6OujYQohBMLDw+Ht7W109k0NzS/5xo0b49tvv9WbZ/Lkybh06ZLB5jVDzp07hy5dusDNzQ2xsbFSk56lWapGA1A3bURGRuL48eOYOXMmpk6dWux9OXPmDF544QVkZ2dj1apVeO211wDo//8ZEhKChw8fFjs7rkZGRgauXr0KuVyudx4VqtwYaJBZFDfdd0mmAzf1oZqbmwt7e3uzPIQPHToEAHj22Wd19mn6P4SFhWlNQlWUSqWCo6Mj8vPz8dhjj5W4DFFRUXjiiSd0pvg29iscUHe400y0ZYym2cfHx6fENQlCCPzzzz8AUOYHaXZ2Ni5cuAAXF5dSPzQCAgLw66+/SgFgcUpSi1OSGrITJ05g6NChePDgAVq2bClNf18eSvO5N+U+KJVKvPrqq4iJiYGNjQ3u37+P/Pz8YgMNmUyGGjVqIC4uDkOHDsXvv/+O1atXG2xiMxRkKBQKHDhwAPn5+ejbty8A4LvvvkNBQQFatWpVboEcmQ8DDSqzgoICqf206GgGzcNJ0z/AFKZ+Wefm5sLd3b3MX+5HjhzBtWvX4OLiovNL/cGDB1i8eDEAaK3zoE9CQgLy8/NhZ2dX7MiUojZt2oQ33ngDffv21enYZ+xXOAC8+OKLJgUami/8d955x2i7uj6ZmZlSc0JJf/EXtXbtWuTm5iIiIqJED/Wi5HK5yX1mzBGMFpaRkQFA3UQGAMOGDcP//ve/UtUyFMfQ51vfejqlPZZGXl4eXnvtNWzbtg3Ozs7YunUr+vTpY9KxW7RogZMnT+Ltt9/Gxo0b8c033+D06dOIiorSWbeksIyMDJw9exZHjhxBdHQ0YmJikJWVhTFjxqBv377Izs6WRmEV1xx59+5dqYaJKg8GGlRmhYMIQ6MZShNoREZGGhzeCqiHAhbOf+vWLYM99TVDSfUtvvTFF18AUPfeL9zEo1Kp8Oqrr+Lhw4do2LChwQW3NDT9PBo0aGBSW7im/Lt378amTZtgY2OD0NBQqFQq2NjYmDR8EDC9tqgkzVfGmPqrWJ/U1FQsXLgQgHrhrvJi6r00xb59+6RF7gBg9uzZRvuJlFXh4KDwdWiC+x9++MHkQMpYoHH37l0MGDAAMTEx8PHxwc8//2ywiUxz7qKcnJywYcMGtG7dGuPGjcPFixfx1FNPYe/evVKz5MOHD7F48WLExcUhNjYW165d07qu0NBQdO7cWZqafcSIEbhz5w5q1qyJkSNHGr2+EydOoHfv3njiiSdw+PBhk5tmyLIYaJDJDH1Jubq6luiLPDs72+h+zUOquF9smgd14Yda4bVC9CkoKNB6UJ49e1Zat0OznoIm34gRI/Dzzz/DwcEB3377rdFf8kIIrF+/HgBM6jMAAH/++ScA9Rdv7dq18e2336JTp05aZShOXl6etFJqcQ8bcz5si6P5tylMqVRi8ODBSEpKQmhoKPr376/3vZpylrS8xvJr7qUp/V0MBUkaP/zwAwD1/V6xYoXOiImSlq04hT/ThT8Tmv8fLi4uOqv2FpWVlYWUlBSDn6ljx46hT58+uHfvHgIDA7Fv3z6tUR9CCOTm5iIvLw93796VRo4Z+j8xZswYBAUFYeDAgXj48CEuXrwoBRpeXl7466+/pGP4+Piga9eu6Ny5Mzp16iTVBqpUKrz99tv4/vvvIZPJsGbNGq3AQfPvqOmoDUCq+ZPL5QwyKhEGGmSy0lTVaty6dQtXrlzBo0ePsGTJEgCGe8Brvlj37NljdB4NTS2JJr8p82goFArpl31eXh5GjBgBIQQaNWqEjh07AlB37nvzzTdx4sQJ2NvbY9u2bWjdurXOMQcNGoTExETIZDLcuXNHar4wZeSBSqXCyZMnAQAtW7bEnj17EBAQoJXHWC1QQkICnn/+edy4cUPKV1zHSk3Nx+zZszF79uxiy2hIcQ/iwufSPNgyMjIwZMgQHDhwAHK5HN9++63BZoai/66m0pxLX/k0n11TRp0UV0O0atUqxMbGYvjw4cU2pxUtmynBY1GaZhpA+/+g5u/PP/+8yfNoGPpMhYWFYdSoUdi2bRv27dun0/SXmZmJ4OBgPHjwQCvdWJNInz59sGPHDiQlJeH111/X2jd37lxs2bIFgwYNwjPPPKNTS5acnIzXX39dWghw7ty56Nevn1YezeiTVatW4aeffoJKpcKdO3cAAFOmTDFYLip/DDTIZCX94i9MqVSiR48eWg+Bp556Sm9ee3t7+Pj4GPy11KhRI9ja2kpfTvn5+fDx8TE60uONN95Abm6u1q9ZzeyWZ8+exSeffAKZTIY//vgDnTt3RkFBAVxcXLB161b07t1b7zHbtGmj84XWqVMnvPTSSwbLoWFjY4O9e/fizTffxObNm+Hh4aGTx1jNT1BQEBo1aoS4uDjUqlULAwcORPfu3Y2eUzPPQ1k7g5rSVKPJo/nzvffew969ewEAixYtMrpSquZhWJLmNuC/B6++h7nmXpoy6kQzmZghcrkcBw8eLFFTlOb/TmmC9cIP98I1RaU5lqF7KpfL8fHHH2PGjBl6mx/d3NwwYsQIaUVfT09PDBgwAC+88ILR8xn6v/P000/j6aefNvi+BQsW4LfffoNMJsPChQsxefJknTxjxoxBdHQ0/v77b9y+fRuAuulm9OjRlW5oebVXnvOdU9V27NgxnbVOSiIsLEwAEI0aNRJz584VOTk55i1gKR04cEBr++uvvxZPPvmkuHLlitH33bx5U8yaNUt8+eWX4rfffhN37tyxZDF13Lhxw+gCV0V17txZABCzZs0q8bk0a5cAEMePHy82/+XLl6W1Q4RQrwXSv39/sXjx4mLfO2vWLAFAdOrUqURlHD16tAAgPvjgA519mlVs7969W6Jjmstjjz0mAIg5c+aU+L0KhUIkJCSIhIQEncXaytPly5fF5s2bRWxsrFAqlRY/35YtWwwuWkdVi0yIcmy4pWotNjYWvr6+xbYnVwb6Oo1Wdffu3YNCoYC7u3uFtF9rOrkSUfXCQIOIiIgshj8viIiIyGIYaBAREZHFMNAgIiIii2GgQURERBbDQIOIiIgshoEGERERWQwDDSIiIrIYBhpERERkMQw0iIiIyGIYaBAREZHFVOvVW1UqFZKSkuDm5qa1qicREREZJ4RARkYG/P39ja5jVK0DjaSkJAQFBVV0MYiIiKqshIQEBAYGGtxfrQMNNzc3AOqbVBGrWRIREVVV6enpCAoKkp6lhlTrQEPTXFJRy2YTERFVdcV1PWBnUCIiIrIYBhpERERkMQw0iIiIyGIYaBAREZHFMNAgIiIii2GgQURERBZTrYe3EhERlRulEoiJAZKTAT8/oH17QC6v6FJZHAMNIiIiS4uKAsaNA+7c+S8tMBBYvhyIjKy4cpUDNp0QERFZUlQUMGCAdpABAImJ6vSoqIopVzlhoEFERGQpSqW6JkMI3X2atPHj1fmsFAMNIiIiS4mJ0a3JKEwIICFBnc9KMdAgIiKylORk8+arghhoEBERWYqfn3nzVUEMNIiIiCylfXv16BJDK5zKZEBQkDqflWKgQUREZClyuXoIK6AbbGi2ly2z6vk0GGgQERFZUmQksGMHEBCgnR4YqE638nk0OGEXERGRpUVGAn37cmZQIiIishC5HIiIqOhSlDs2nRAREZHFMNAgIiIii2GgQURERBZTqQKN1NRUhISE4ObNm3r3P//889i4caO0/ccff6Bx48bw9fXFkiVLyqeQREREZLJKE2ikpqaiV69eBoOMzZs347fffpO27927hz59+mDIkCE4evQoNm/ejEOHDpVTaYmIiMgUlSbQGDx4MF566SW9+x48eIBJkyahYcOGUtrmzZvh7++PmTNnon79+vjwww+xfv368iouERERmaDSBBpr167F2LFj9e6bNGkS+vXrh/DwcCnt3Llz6NixI2T/P7NamzZtcOrUKaPnUCgUSE9P13oRERGR5VSaQCMkJERv+qFDh3DgwAEsXLhQKz09PV3rPe7u7khKSjJ6jvnz58PDw0N6BQUFlb3gREREZFClCTT0yc3NxahRo7B69Wq4ublp7bO1tYWDg4O07ejoiOzsbKPHmzZtGtLS0qRXQkKCRcpNREREapV6ZtCPP/4YYWFh6Nmzp84+b29v3Lt3T9rOyMiAvb290eM5ODhoBSdERERkWZU60NiyZQvu3bsHT09PAEB2dja2bduGEydOICwsDFu2bJHynjlzBgFFF6whIiKiClWpA42YmBgUFBRI25MnT0Z4eDiGDRsGABgzZgx+//13dOjQAQsXLkS3bt0qqKRERESkT6UONAIDA7W2XV1d4evrC19fXwDA0qVL0aNHD7i6usLT01NrMi8iIiKqeDIhhKjoQpRFfHw8Ll++jPbt28PV1bVE701PT4eHhwfS0tLg7u5uoRISERFZH1OfoZW6RsMUISEhBofGEhERUcWq1MNbiYiIqGpjoEFEREQWw0CDiIiILIaBBhEREVkMAw0iIiKyGAYaREREZDFVfngrERFRlaBUAjExQHIy4OcHtG8PyOUVXSqLY6BBRERkaVFRwLhxwJ07/6UFBgLLlwORkRVXrnLAphMiIiJLiooCBgzQDjIAIDFRnR4VVTHlKicMNIiIiCxFqVTXZOhb7UOTNn68Op+VYqBBRERkKTExujUZhQkBJCSo81kpBhpERESWkpxs3nxVEAMNIiIiS/HzM2++KoiBBhERkaW0b68eXSKT6d8vkwFBQep8VoqBBhERkaXI5eohrIBusKHZXrbMqufTYKBBRERkSZGRwI4dQECAdnpgoDrdyufR4IRdRERElhYZCfTty5lBiYiIyELkciAioqJLUe7YdEJEREQWw0CDiIiILIaBBhEREVkMAw0iIiKyGAYaREREZDEMNIiIiMhiGGgQERGRxTDQICIiIothoEFEREQWw0CDiIiILIaBBhEREVkMAw0iIiKyGAYaREREZDEMNIiIiMhiGGgQERGRxTDQICIiIothoEFEREQWw0CDiIiILIaBBhEREVkMAw0iIiKyGAYaREREZDEMNIiIiMhiGGgQERGRxTDQICIiIothoEFEREQWw0CDiIiILKZSBRqpqakICQnBzZs3pbRdu3ahXr16sLW1RYsWLXDp0iVpX2xsLMLCwuDl5YUpU6ZACFEBpSYiIiJDKk2gkZqail69emkFGdevX8fw4cPx6aefIjExEQ0aNMAbb7wBAFAoFOjduzdatWqFkydPIi4uDhs3bqyYwhMREZFelSbQGDx4MF566SWttEuXLuHTTz/FoEGDUKtWLYwePRpnzpwBAPzyyy9IS0vDkiVLEBoainnz5mH9+vUVUXQiIiIywLaiC6Cxdu1ahISEYNy4cVJar169tPJcuXIF9evXBwCcO3cO4eHhcHZ2BgA0a9YMcXFxRs+hUCigUCik7fT0dHMVn4iIiPSoNDUaISEhRvfn5eVh8eLFeOuttwCog4TC75HJZJDL5Xj48KHBY8yfPx8eHh7SKygoyDyFJyKi0lEqgehoYOtW9Z9KZUWXiMys0gQaxZk1axZcXFykPhq2trZwcHDQyuPo6Ijs7GyDx5g2bRrS0tKkV0JCgkXLTERERkRFAcHBQMeOwEsvqf8MDlank9WoNE0nxhw8eBArV67EsWPHYGdnBwDw9vZGbGysVr6MjAzY29sbPI6Dg4NOcEJERBUgKgoYMAAoOlowMVGdvmMHEBlZMWUjs6r0NRrx8fEYMmQIVq5ciSZNmkjpYWFhOHr0qFY+hUIBb2/viigmERGZSqkExo3TDTKA/9LGj2czipWo1IFGTk4OevXqhb59+6Jfv37IzMxEZmYmhBB49tlnkZ6ejg0bNgAA5s2bh86dO0Mul1dwqYmIyKiYGODOHcP7hQASEtT5qMqr1E0n+/btQ1xcHOLi4rB27VopPT4+HsHBwVi3bh2GDBmCKVOmwMbGBtHR0RVXWCIiMk1ysnnzUaVW6QKNwrN79u3b1+hsn3369MH169dx6tQphIeHw8fHpzyKSEREZeHnZ958VKnJRDWetzs9PR0eHh5IS0uDu7t7RReHiKh6UCrVo0sSE/X30wAAHx/g338BNodXWqY+Qyt1Hw0iIrJCcjmwfLnhIAMA7t8Hdu0qvzKRxTDQICKi8te3r7rWwhCZjCNPrAQDDSIiKn8xMepaC0M48sRqMNAgIqLyx5En1QYDDSIiKn8ceVJtMNAgIqLy1749EBio7ouhj0wGBAWp81GVxkCDiIjKn2bkCaAbbGi2ly3j8FYrwECDiIgqRmSkevG0gADt9MBALqpmRSrdzKBERFSNREaqh7rGxKg7fvr5qZtLWJNhNRhoEBFRxZLLgYiIii4FWQibToiIiMhiGGgQERGRxTDQICIiIothoEFEREQWw0CDiIiILIajToiISkqp5HBMIhMx0CAiKomoKGDcOODOnf/SAgPVs1xygikiHWw6ISIyVVQUMGCAdpABAImJ6vSoqIopF1ElxkCDiMgUSqW6JkMI3X2atPHj1fmISMJAg4jIFDExujUZhQkBJCSo8xGRpNR9NGJjY/H999/j3LlzSEpKQk5ODvz8/BAcHIw+ffqge/fusLOzM2dZiYgqTnKyefMRVRMlrtG4cOEC2rVrh6eeegrXrl1Dt27dMGPGDCxevBgvvfQSfHx8MGHCBPj7+2PdunWWKDMRUfnz8zNvPqJqQiaEvgZH/ebOnYu5c+diwoQJmDFjBpydnQ3m3bNnD8aOHYvg4GDs3LkTnp6e5iivWaWnp8PDwwNpaWlwd3ev6OIQUWWmVALBweqOn/q+NmUy9eiT+HgOdaVqwdRnqEk1GiqVCq+//jpWr16NI0eOYO7cuUaDDADo3bs3YmNj4ebmhmeeeQaJiYkluwIiospELlcPYQXUQUVhmu1lyxhkEBVhUqBhY2ODRo0a4fDhw2jRooXJB3dxccHOnTsxaNAguLm5lbaMRESVQ2QksGMHEBCgnR4YqE7nPBpEOkrUdGJt2HRCRKXCmUGJTH6GWnRm0OXLl8PFxQWvv/46ZEWrGomIqiq5HIiIqOhSEFUJZZpH4++//8bnn38OpZ4JatLT0/HRRx9h1KhRGDVqVFlOQ0RERFVUmQKNP//8ExMmTNC7z93dHRcvXsS4ceOwZcuWspyGiIiIqiiTA43bt2/D398fWVlZUpqDgwNsbGwgN9A26e/vjzFjxiAnJwcqlarspSUiIqIqxeRAw8HBASkpKXBycpLSZDIZ+14QERGRQSZ3BrW1tYVMJoONTclaW9LT00tcKCKiMuPIEKJKocyjTpRKJV577TWD+48fPw5nZ+cSByhERKUWFaVeabXwImiBgeoJtzjXBVG5KnOgIYTAoUOHDO53cXHBggULynoaIiLTREUBAwboThOemKhO58RaROWqzIGGra0tEhISzFEWIqKyUSrVNRn65iEUQj1V+PjxQN++bEYhKidszyAi6xETo91cUpQQQEKCOh8RlQuzNJ1s2bIFdnZ2CAwMRNOmTbmuCRFVjORk8+YjojIzS2fQV155BYB6uKutrS06dOiA8ePHo0ePHmUuIBGRyfz8zJuPiMrMLH00Dhw4gPz8fNy6dQuHDx/G7t270bt3b/Tu3RubNm2Ch4eHOcpKRGRc+/bq0SWJifr7achk6v3t25d/2YiqKZMDDc0iryqVSmeoavtC/2mHDx+OrKwszJ8/H59++ik6dOiA6OhoeHp6mqfERESGyOXqIawDBqiDisLBhmZywWXL2BGUqByZ3Bk0OzsbQghkZ2dLaUII6Ftl3sXFBZ988gm2bNmC2NhYDBkyxDylJSIqTmSkeghrQIB2emAgh7YSVQCTazQCAgIQHx8PV1dXKS0/P9/oGiaDBg3CmTNnsHDhQmzatAlDhw4tW2mJiEwRGakewsqZQYkqnMmBhlwuR926dbXSfH198fTTT0OhUMDBwUHv+2bNmoWvv/4aderUKVtJiYhKQi4HIiIquhRE1Z5M6Gv7MLN//vkHDRo0sPRpSiw9PR0eHh5IS0uDu7t7RReHiIioyjD1GVouE3aZGmSkpqYiJCQEN2/elNJiY2MRFhYGLy8vTJkyRatPyB9//IHGjRvD19cXS5YsMXexiYiIqIwqzcygqamp6NWrl1aQoVAo0Lt3b7Rq1QonT55EXFwcNm7cCAC4d+8e+vTpgyFDhuDo0aPYvHmz0TVXiIiIqPyZHGhkZmaiZs2auH37tkUKMnjwYLz00ktaab/88gvS0tKwZMkShIaGYt68eVi/fj0AYPPmzfD398fMmTNRv359fPjhh9I+IiIiqhxMDjQcHR2RmpoKe3t7ixRk7dq1GDt2rFbauXPnEB4eDmdnZwBAs2bNEBcXJ+3r2LEjZP8/Nr5NmzY4deqU0XMoFAqkp6drvYiIiMhyTB51Ymtrq/UnoJ686/PPP4e3tze8vb3h4+MDHx8f+Pr6wtvbu0QFCQkJ0UlLT0/XSpfJZJDL5Xj48CHS09PRpEkTaZ+7uzuSkpKMnmP+/PmYPXt2icpFREREpVfiKcgLzwqan5+PiRMnQiaTQQgh1S5o8vn4+KB58+b44IMPtGYPNblwtrY6w2YdHR2RnZ2ts0+Tbsy0adMwceJEaTs9PR1BQUElLhcRERGZpsxrnQDA9u3b4eLigrS0NKSlpeHBgwdITU1FQkICDhw4gBdeeAF37tyBk5NTiY7r7e2N2NhYrbSMjAzY29vD29sb9+7d00k3xsHBweB8H0RERGR+Zgk0WrdubXBCrl9++QU9e/bEoUOHSryaa1hYGNauXSttx8fHQ6FQwNvbG2FhYdiyZYu078yZMwgoOuUwERERVSiLD2+tX78+AOD8+fMlfu+zzz6L9PR0bNiwAQAwb948dO7cGXK5HH369MFff/2F33//Hfn5+Vi4cCG6detm1rITERFR2ZilRsMYBwcHzJ49u1TrnNja2mLdunUYMmQIpkyZAhsbG0RHRwNQT3++dOlS9OjRA66urvD09JTm2CAiIqLKoURTkNvY2CA1NVUaUaJQKODk5ISbN29adC2TlJQUnDp1CuHh4fDx8dHaFx8fj8uXL6N9+/ZaC76ZglOQExERlY6pz9AS12js378fgYGB8PDwgIODA2QymdZoE0uoXbs2evbsqXdfSEiI3qGxREREVPFKXKNRNKgQQiA0NBQRERGIiIhAz5494enpae5yWgRrNIgAKJVcTp2ISszUZ2iJA43vvvsOtra2yM7ORnJyMq5fv47Lly/j2LFjyMvLg4uLC4YNG4bx48cjNDTULBdjKQw0qNqLigLGjQPu3PkvLTAQWL4ciIysuHIRUaVnsUCjcB+NwnJycrB//36sWLECBw4cgIODA+bOnas1QVZlw0CDqrWoKGDAAKDoV4Cm1nLHDgYbRGRQuS8T7+TkhD59+mD//v2IiYlBaGgopkyZgq5duyIrK8tcpyEic1Aq1TUZ+n5naNLGj1fnIyIqA5MDDc0U40oTvnieeeYZnD59GiNGjMDvv/+O3r17Q6FQlKmgRGRGMTHazSVFCQEkJKjzERGVgcmBRl5eHoQQyMvLMym/vb091q5di/feew/R0dFYvnx5qQtJRGaWnGzefEREBpg8vFUIgffee09ast1U8+fPh0KhqNR9NYiqHT8/8+arCji6hqhClKgzqLVhZ1CqtpRKIDgYSEzU309DJlOPPomPt46HMUfXEJlduXcGJaIqRC5XP2SB/0aZaGi2ly2zniBjwADdPimJier0qKiKKRdRNWFSoKFSqdC7d2/8+uuvJT5BSkoKOnXqhKtXr5b4vURkQZGR6iGsRVc9Dgy0nqGtHF1DVOFMCjRsbGzQv39/9O/fH2vWrDH54OfPn0f79u1Ro0YNBAcHl7aMRGQpkZHAzZvAoUPAli3qP+PjrSPIADi6hqgSMLnpZNiwYdizZw/mzZuHZ599FjFG/mMmJCRgypQpeOqppzBo0CBs3boVdnZ2ZikwEZmZXA5ERABDhqj/tIbmEg2OriGqcCVaVO25557DpUuXMGfOHPTr1w9OTk546qmnEBAQACcnJyQlJeHKlSs4deoUOnXqhKNHj6JZs2aWKjsRkXHVcXQNwBE2VKmUetRJQUEBDh48iPPnzyMpKQm5ubmoXbs2goOD0b17d9SoUcPcZTU7jjohsnLVbXQNwBE2VG4sstaJtWGgQVQNaEadANrBhjWu6cL1a6gccXgrERFQPUbXABxhQ5VWifpoEBFVSZGRQN++1t1voSQjbCIiyq1YRAw0iKh60IyusVYcYUOVlNmaTtLS0sx1KCIiKqnqOsKGKj2zBRpbtmxBmzZtuBw8EVFFaN9e3e+k6JTyGjIZEBSkzkdUjkrcdLJ27Vp06NABDRo00Eo/ePAg5HI5Zs6cCUdHR9ja/nfogoIC5ObmYuHChWUvMRER6dKsXzNggDqo0DfCxlrWr6EqpcTDW7t3747ff/8d/fv3x8KFC1GnTh1kZmbCz88Pn3/+OV5//XXtE8hkEEJAJpNBWcl6O3N4KxFZHX3zaAQFqYMMaxlhQ5WCxebR6N69Ozw9PXH69Gn8+++/+Oqrr5CYmIgFCxbgxo0bcHR0xPXr11GnTh0IIWBvb49bt27B398f8koWSTPQICKrxJlBqRyY+gwt1aiTjh074ptvvsGUKVMwaNAguLi4YPHixbC3twcAyOVyraCi6DYREVmQtY+woSql1J1BbW1tMXfuXISGhiI3Nxf9+vUzZ7mIiIjICpgcaHz++edYtGgRCgoKAABJSUno3Lkz3Nzc8OSTT2LmzJkWKyQRERFVTSY3ncTExOCHH36A7P97L0+dOhU9evTA2rVrceXKFTz99NOYNm0aAODs2bNISUkBoO4MevbsWdy5cwetW7eGjQ1nPSciIqouStQZ9MyZM5g9ezZ2796Nxx57DEeOHIGvry8AoFu3bmjVqhU+/fRTKRgBAM3hZTIZ7t27B29vbzNfQumxMygREVHpWKQzaMuWLfHjjz9i//79GDlyJFq3bo0///wTderUwbBhwzB16lR88803cHFxga2tLWQyGVQqFZRKJXJzc+Hm5lbmCyMiIqKqo1SjTtq1a4f169fjyy+/hL+/PwAgLCwM9+7dQ5s2bVC/fn2zFpKIiIiqplIFGv/88w+6du2KnJwcpKSkYOzYsThz5gwOHz7MIIOIiIgkJgcaSUlJ0tTieXl5AIDc3Fz89ttvuHz5Mj777DPUrFkTt2/f1vt+lUqFoKAgzqdBRERUjZjcGdTLywvp6emlOolmCvJLly7prJFSkdgZlIiIqHTM3hl0y5YtsLe3h62tLW7evInhw4ejVatWOHXqFGxsbPDCCy9g5MiRcHR01HmvUqlETk4OAgICSnc1REREVCWZHGh0795d+runpydkMhmOHDmCxMRErFmzBv/73/9w7NgxfPTRRxgxYgTnyyAiIqLSTUGuUCgghEBOTg6Cg4Px6aefIj4+HsOGDcO7776L5s2bIy0tzdxlJSIioiqmxKu3Auo+F9nZ2XBxcdHZd+XKFezZsweTJ082SwEtiX00iIiISsfUZ2ipajRkMpkUZKxYsUJrX8OGDatEkEFERESWV6aOFNu3b8f48ePxxRdfmKs8REREZEVK1XQCqKtMWrRoAaVSiYsXL8LV1dXcZbM4Np0QERGVjkXWOils5MiR+Pfff3Hw4EFs3rwZcrkcdnZ2WguqaSgUCuTm5uLdd98t7emIiIioCipVjcbo0aPx1VdfYdu2bejbty/c3NyQlZVl/EQyGZRKZakLagms0SAiIiods9doFBQUICMjA6+//joOHDiAPXv2oGvXrgCAuLg4aTIvffLy8qBQKEp4CURERFTVmRxovPDCCzh9+jT8/f1x6tQpPPbYY9K+oKAgixSOiIiIqjaTRp08evQIt27dQkpKCjIzM5GSkmLpcmlZt24dgoKC4OzsjIiICNy4cQMAEBsbi7CwMHh5eWHKlCkoZb9WIiIishCTAg1PT09cuHAB+/fvh7u7OyIiIvDRRx9J+1esWIHVq1dj9+7dOHHiBG7evAmVSmWWAl6/fh1z5szBrl27cPnyZYSGhmLYsGFQKBTo3bs3WrVqhZMnTyIuLg4bN240yzmJiIjITEQpLF++XDg6OoqBAweKgoICERAQIGQymfSysbERzs7OokePHuLgwYOlOYVk+/btYuDAgdL24cOHhZ+fn9i5c6fw8vISWVlZQgghzp49K5555pkSHTstLU0AEGlpaWUqIxERUXVj6jO01PNoHDx4EH379sXzzz+PLVu2QCaTITc3FxkZGUhOTsbx48excuVKXLp0CWPGjMHy5cv1Dn0tTlxcHJ599ln8/vvvCAkJwdtvvw1bW1vUq1cPx48fx88//6wJmODj44MHDx4YPJZCodDqlJqeno6goCCOOrEmSiUQEwMkJwN+fkD79oBcXtGlIiKyOiaP3CxLNLNr1y4hl8vFtGnT9O5XKpViwoQJQiaTiaFDh5b6PKNGjRIABAAREhIi7t69KyZOnCjefvttrXy+vr7iwYMHBo8za9Ys6TiFX6zRsBI//CBEYKAQwH+vwEB1OhERmZWpNRomT0G+fv16pKamaqX16dMHkyZNwtWrV/W+x8bGBkuWLMHo0aPRoEEDU0+l5cSJE9izZw+OHTuGR48eYciQIejRowdsbW3h4OCgldfR0RHZ2dkGjzVt2jSkpaVJr4SEhFKViSqhqChgwADgzh3t9MREdXpUVMWUi4iomjOp6eTu3bsIDAyEnZ0dhg0bhnHjxkmBQ0FBAWxsbGBjYzhmUSgUyMvLg5ubW4kLOGHCBNjY2GDx4sUA/msiee+99xAbG4tvvvlGyuvp6YmrV6+iRo0aJh2bE3ZZCaUSCA7WDTI0ZDIgMBCIj2czChGRmZh19VZ3d3d8//33GDRoEDZu3IjGjRtDLpdDLpfDwcEBdnZ20ra+l7OzM7y8vEp1ISqVCnfv3pW2MzIykJ2dDVtbWxw9elRKj4+Ph0KhgLe3d6nOQ1VYTIzhIANQN6IkJKjzERFRuTJpwi5HR0f069cP/fr1w5IlS7BmzRosXLgQaWlpeOKJJ/DOO+/A3t5e73s1HTALCgpKVcD27dtj6NChePLJJ1GrVi2sW7cOtWvXxtixY7FgwQJs2LABw4cPx7x589C5c2fI+Yu1+klONm8+IiIym1KPOrl//z6mT5+OdevWoXHjxvj666/x5JNPmrt8EELgk08+wbp165CcnIymTZti/fr1aNmyJXbv3o0hQ4bAyckJNjY2iI6ORpMmTUw+NptOrER0NNCxY/H5Dh0CIiIsXRoiomrB1GdoqQMNjZ07d2Lo0KFYsmQJ3njjjbIcqlRSUlJw6tQphIeHw8fHp0TvZaBhJTR9NBIT1c0kRbGPBhGR2ZVboAEAiYmJCAgIKOthyh0DDSuiGXUCaAcbmrlbduwAIiPLv1zmxnlCiKiSMGtn0OJUxSCDrExkpDqYKPpZDAy0niAjKkpdc9OxI/DSS+o/g4M5dJeIKjWz1GhUVazRsELW+otfU2NT9L+rtdXYEFGVUa5NJ1UVAw2qEjhPCBFVQuXadEJEFsR5QoioCmOgQVTZcZ4QIqrCGGgQVXZ+fubNR0RUjhhoEFV27dur+2BoOn4WJZMBQUHqfERElQwDDaLKTi4Hli9X/71osKHZXraMHUGJqFJioEFUFVSHeUKIyCqZtKgaEVUCkZFA377WOU8IEVktBhpEVYlczoXhiKhKYdMJERERWQwDDSIiIrIYBhpERERkMQw0iIiIyGIYaBAREZHFcNQJVRxrXdKdiIgkDDSoYkRFAePGaa9KGhiongGTk08REVkNNp1Q+YuKAgYM0F36PDFRnR4VVTHlIiIis2OgQeVLqVTXZAihu0+TNn68Oh8REVV5DDSofMXE6NZkFCYEkJCgzkdERFUeAw0qX8nJ5s1HRESVGgMNKl9+fubNR0RElRoDDSpf7durR5fIZPr3y2RAUJA6HxERVXkMNKh8yeXqIayAbrCh2V62jPNpEBFZCQYaVP4iI4EdO4CAAO30wEB1OufRICKyGpywiypGZCTQty9nBiUisnIMNKjiyOVARERFl4KIiCyIgQZRZcG1X4jICjHQIKoMuPYLEVkpdgYlqmhc+4WIrBgDDaKKxLVfiMjKMdAgqkhc+4WIrBwDDaKKxLVfiMjKMdAgqkhc+4WIrBwDDaKKxLVfiMjKMdAgqkhc+4WIrBwDDaKKxrVfrJdSCURHA1u3qv/k6CGqhjhhF1FlwLVfrA8nYSMCAMiE0DeAv3pIT0+Hh4cH0tLS4O7uXtHFISJroZmErejXq6Y5jDVVZAVMfYay6YSIyJw4CRuRFgYaRETmxEnYiLQw0CAiMidOwkakhYEGEZE5cRI2Ii1VLtB477330Lt3b2k7NjYWYWFh8PLywpQpU1CN+7YSUWXASdiItFSpQOP8+fNYtWoVlv//BEcKhQK9e/dGq1atcPLkScTFxWHjxo0VW0giqt44CRuRlioTaKhUKrz55puYMGEC6tWrBwD45ZdfkJaWhiVLliA0NBTz5s3D+vXrK7ikRFTtcRI2IkmVCTTWrFmDCxcuIDg4GLt370ZeXh7OnTuH8PBwODs7AwCaNWuGuLg4g8dQKBRIT0/XehERWURkJHDzJnDoELBli/rP+HgGGVTtVIlAIzMzE7NmzUK9evVw69YtLF26FO3atUN6ejpCQkKkfDKZDHK5HA8fPtR7nPnz58PDw0N6BQUFldclEFF1JJcDERHAkCHqP9lcQtVQlQg0oqKikJWVhUOHDmH27NnYv38/MjIy8NVXX8HBwUErr6OjI7Kzs/UeZ9q0aUhLS5NeCQkJ5VF8IiKiaqtKrHVy584dhIeHw9fXFwBga2uLZs2a4fLly7h3755W3oyMDNjb2+s9joODg05gQkRERJZTJWo0AgMDkZOTo5V269YtLFu2DEePHpXS4uPjoVAo4O3tXd5FJCIiIj2qRKDRs2dPxMXFYc2aNbhz5w4+//xznDt3DpGRkUhPT8eGDRsAAPPmzUPnzp0hZzsoERFRpVAlmk58fHzw888/Y/LkyZg4cSL8/Pywbds2BAUFYd26dRgyZAimTJkCGxsbREdHV3RxiYiI6P9ZxTLxKSkpOHXqFMLDw+Hj42Py+7hMPBERUemY+gytEjUaxalduzZ69uxZ0cUgIiKiIqpEHw0iIiKqmhhoEBERkcUw0CAiIiKLYaBBREREFsNAg4iIiCyGgQYRERFZDAMNIiIishgGGkRERGQxDDSIiIjIYhhoEBERkcUw0CAiIiKLYaBBREREFmMVi6qREUolEBMDJCcDfn5A+/aAXF7RpSIiomqCgYY1i4oCxo0D7tz5Ly0wEFi+HIiMrLhyERFRtcGmE2sVFQUMGKAdZABAYqI6PSqqYspFRETVCgMNa6RUqmsyhNDdp0kbP16dj4iIyIIYaFijmBjdmozChAASEtT5iIiILIiBhjVKTjZvPiIiolJioGGN/PzMm4+IiKiUGGhYo/bt1aNLZDL9+2UyIChInY+IiMiCGGhYI7lcPYQV0A02NNvLlnE+DSIisjgGGtYqMhLYsQMICNBODwxUp3MeDSIiKgecsMuaRUYCfftyZlAiIqowDDSsnVwORERUdCmIiKiaYtMJERERWQwDDSIiIrIYBhpERERkMQw0iIiIyGIYaBAREZHFcNRJeVIqOdSUiIiqFQYa5SUqSr10e+FVVQMD1TN4cvIsIiKyUmw6KQ9RUcCAAbpLtycmqtOjoiqmXERERBbGQMPSlEp1TYYQuvs0aePHq/MRERFZGQYalhYTo1uTUZgQQEKCOh8REZGVYaBhacnJ5s1HRERUhTDQsDQ/P/PmIyIiqkIYaFha+/bq0SUymf79MhkQFKTOR0REZGUYaFiaXK4ewgroBhua7WXLOJ8GERFZJQYa5SEyEtixAwgI0E4PDFSncx4NIiKyUpywq7xERgJ9+3JmUCIiqlYYaJQnuRyIiKjoUhAREZUbNp0QERGRxTDQICIiIothoEFEREQWU+UCjeeffx4bN24EAPzxxx9o3LgxfH19sWTJkootGBEREemoUoHG5s2b8dtvvwEA7t27hz59+mDIkCE4evQoNm/ejEOHDlVwCYmIiKiwKhNoPHjwAJMmTULDhg0BqIMOf39/zJw5E/Xr18eHH36I9evXV3ApiYiIqLAqM7x10qRJ6NevH3JycgAA586dQ8eOHSH7/9k127Rpg/fff9/oMRQKBRQKhbSdlpYGAEhPT7dQqYmIiKyT5tkphDCar0oEGocOHcKBAwdw8eJFvPvuuwDUF9ikSRMpj7u7O5KSkoweZ/78+Zg9e7ZOelBQkHkLTEREVE1kZGTAw8PD4P5KH2jk5uZi1KhRWL16Ndzc3KR0W1tbODg4SNuOjo7Izs42eqxp06Zh4sSJ0vajR49Qt25d3L592+hNItOkp6cjKCgICQkJcHd3r+jiWAXeU/Pi/TQ/3lPzqkr3UwiBjIwM+Pv7G81X6QONjz/+GGFhYejZs6dWure3N+7duydtZ2RkwN7e3uixHBwctIITDQ8Pj0r/D1qVuLu7836aGe+pefF+mh/vqXlVlftpyo/0Sh9obNmyBffu3YOnpycAIDs7G9u2bQMAPP3001K+M2fOIKDoomVERERUoSp9oBETE4OCggJpe/LkyQgPD8ewYcMQFBSE33//HR06dMDChQvRrVu3CiwpERERFVXpA43AwECtbVdXV/j6+sLX1xdLly5Fjx494OrqCk9PT2kiL1M5ODhg1qxZeptTqOR4P82P99S8eD/Nj/fUvKzxfspEceNSKrn4+HhcvnwZ7du3h6ura0UXh4iIiAqp8oEGERERVV5VZmZQIiIiqnoYaBAREZHFVMtAgyvAmtd7772H3r17S9uxsbEICwuDl5cXpkyZUuz0tKS2bt06BAUFwdnZGREREbhx4wYA3s+SSk1NRUhICG7evCmlGbuH/A4onr57umvXLtSrVw+2trZo0aIFLl26JO3jZ9Y4ffezsMLPKKDqf0arXaDBFWDN6/z581i1ahWWL18OQL2eTO/evdGqVSucPHkScXFxJR4NVB1dv34dc+bMwa5du3D58mWEhoZi2LBhvJ8llJqail69eml9gRu7h/wOKJ6+e3r9+nUMHz4cn376KRITE9GgQQO88cYbAPgdUBx997Owws8owEo+o6IauX//vqhVq5Zo2LCh2LBhg1i6dKlo1KiRUKlUQgghfvzxR/Hyyy9XcCmrDqVSKdq2bStmzpwppe3cuVN4eXmJrKwsIYQQZ8+eFc8880xFFbHK2L59uxg4cKC0ffjwYeHn58f7WUKdOnUSy5cvFwBEfHy8EML4Z5LfAcXTd0/37NkjvvzySynPwYMHhZOTkxCC3wHF0Xc/NYo+o4Swjs9otarR0KwAGx4eDkD/CrCnTp2qyCJWKWvWrMGFCxcQHByM3bt3Iy8vD+fOnUN4eDicnZ0BAM2aNUNcXFwFl7Tya9KkCQ4ePIizZ88iLS0Nq1atQpcuXXg/S2jt2rUYO3asVpqxe8jvgOLpu6e9evXCm2++KW1fuXIF9evXB2D8fpP++6lR9BkFWMdntNoEGpoVYBcuXCilpaenIyQkRNo2ZQVYUsvMzMSsWbNQr1493Lp1C0uXLkW7du107qlMJoNcLsfDhw8rsLSVX5MmTTBgwAC0bNkSnp6eOHr0KD777DPezxIqfK80jN1DfgcUT989LSwvLw+LFy/GW2+9BcD4/SbD91PfMwqwjudUtQg0zLkCLKlFRUUhKysLhw4dwuzZs7F//35kZGTgq6++0pnRjve1eCdOnMCePXtw7NgxPHr0CEOGDEGPHj10PqMA72dJGbuH/A4ou1mzZsHFxUXqo8HPbMkZekYB1vGcqhaBhjlXgCW1O3fuIDw8HL6+vgDU/xmaNWuGR48ead1TgPfVFFu3bsXgwYPRtm1beHh44JNPPsH169d1PqMA72dJGbuH/A4om4MHD2LlypXYsmUL7OzsABi/36SfoWcUYB3PqUq/1ok5cAVY8wsMDEROTo5W2q1bt7Bs2TKsWLFCSouPj4dCoYC3t3d5F7FKUalUSE1NlbYzMjKkX9xHjx6V0nk/Sy4sLAxr166Vtgvfw7CwMGzZskXax+8A08XHx2PIkCFYuXIlmjRpIqUbu9+kn6Fn1IkTJ6zjM1rRvVHLQ0JCgoiPj5de/fv3F4sWLRL37t0Tjo6OYv/+/SIvL088//zz4p133qno4lYJqampwt3dXaxevVokJCSI5cuXC0dHR3H79m1Ro0YN8dVXXwkhhHjjjTdEr169Kri0ld/27duFs7OzWLJkidi8ebPo2LGjqFu3rsjLy+P9LAUU6tGfn59v8B7yO8B0he9pdna2aNKkiRg5cqTIyMiQXiqVyuj9pv8Uvp/GnlHW8BmtFoFGUUOHDpWGDq1evVrY2dkJLy8vERISIlJSUiq2cFXI4cOHRXh4uHBychL16tUTu3fvFkIIsWvXLuHs7Cx8fHxEjRo1xMWLFyu4pJWfSqUSc+bMEXXq1BF2dnaiZcuW4vTp00II3s/SQJGhg8buIb8DTFP4nv74448CgM5Ls5+f2eIV/YwWVvgZJUTV/4xyUTVwBVhLSElJwalTpxAeHg4fH5+KLk6Vx/tZdsbuIb8DzI+fWfOqyp9RBhpERERkMdVi1AkRERFVDAYaREREZDEMNIiIiMhiGGgQERGRxTDQICKDCgoKzHo8pVJp1uOZYseOHRg2bJjZjpeYmIhff/0VCQkJJuWviGsmqkyqxcygRNbo6tWryM7Ohr29vbSyo6mEEFAoFGjSpInWdMaPHj3C2bNn8ddff2H37t0ICQnBd999Z5bybty4Edu3b8fevXultD///BO3b9+Gvb09bGyK/92jVCqRm5uLiIgI1K1b16TzHjlyBJs2bcLzzz+PwYMHl7jcubm5cHR0lLbPnz+PHj16YP369RgxYoSUfuPGDdSsWVNr6OHDhw/xzDPP4Pvvv8cTTzxR4nMTWYUKncWDiEqtZ8+eeidNKskrPj5enDhxQjz55JOiVq1aUvrjjz8u3nnnHbFp0yZx9+5do8do165dsWU9cOCAcHJyEj/99JNW+rBhw4SDg4NwdXUVHh4e0svFxUUAEC4uLlrprq6uwtbWVuzcudPk+3T//n3h6ekpAgIChEKhMPl9BQUFYvDgwaJNmzYiPz9fSv/7778FALF582at/OHh4aJmzZri5MmTWunvv/++CAoKEklJSSafm8iaMNAgqqIyMzNFVlaWUKlUOvtat24tunXrZvC9BQUFIiMjQyiVSlFQUCA+//xzsWfPHnH58mUBQHzzzTdS3ry8PAFAzJs3T1y6dEnrFRYWJrp27Wq0nBkZGSIwMFB88cUXJl/bnj17BACxZ88erfQHDx7oza9QKERWVpZWQFBYVFSUOH/+vN59SqVSKBQKkZubq7PvnXfeEQDEzJkzpbQLFy4IAGLr1q1S2q5duwQA0bNnT73/HgMGDBD9+/fXe34ia8dAg8jKHD16VAAQR44cKdX7iwYaSqVSANCaElmjQ4cOonv37kaPN3XqVPHMM8/ofQCrVCoxbdo0MWXKFK10fYGGSqUSbdu2FR07dtQJChYtWlTm2p1JkybplC87O1vUq1dP2NnZiUuXLgkhdAON+/fvi4CAAFGjRg1x9+5dvfcgJSVFeHp6in379hm9V0TWiH00iKqwtWvX4s0339S7r/DKxEU1b94cZ8+eBaBeKVIIAXt7e9ja/veVoFKpkJeXp5Wmj7G+Ffn5+Vi3bh1++OEHvf1IZDIZbt68ia1bt6Jp06Z47bXXDB7r22+/xfHjxzFq1Cg4ODho7evUqRPWr1+vdQ1fffUVYmJisGHDBqPlVyqVyMvLQ4MGDXT2OTk5Yf78+XjxxRexbds2fPjhhzp5Vq1ahcTERGzfvh01atTQe45atWph7NixWL16Nbp06WK0PERWp6IjHSIqvU2bNgkAIjo6Wly6dEmsXr1aANBq3nj55ZdFu3btpO1OnTqJsLAw6Rhjxowx+kt///79Rms0evbsabB8v/76q/D39xdKpdJgnqysLNG8eXPh6Ogozp07J4TQrdFITk4WtWrVEvXq1RMZGRkm3ZuRI0cKT09Pk/Iao1KpxNGjR6VtfU0nhw8fLvY4ly5dEo6OjiI9Pb3MZSKqSlijQVSFaX7Zh4aGIjAwENeuXQMANGrUSMrj6emJ1NRUKc3V1RUKhULaP2nSJAwfPhwODg749ttvsWDBAixYsAA9evRATk4O6tevX+rynT9/Hk899ZTRWg9nZ2ds3LgRrVu3xuDBg3HmzBmt/SqVCq+88gru37+PqKgokxeUys/P16n5MMXDhw9x8+ZNODg4SOX29PTE5cuXAagXtwKApKQkKc3HxweXL1+GEAL5+fnIz89H/fr14e7uLh23UaNGcHFxwZUrV9C6desSl4uoqmKgQVSFyeVyvel37tyR/p6VlWX0fSEhIQgJCQEAHDt2DADg7++Ppk2bAih+Lg1j80QkJSUhICDA6PsBoEWLFhg3bhxCQ0N1goN///0Xd+/exezZs402BxX16NEjyGQyKRgwxM/PDx4eHtL2gQMHMHDgwGKPP2nSJEyaNMng/v3796Nz585aaf7+/khKSir22ETWhIEGkRUKCgrS2u7WrVux7zl//jz+/PNPAOo5IT766CN89NFHyMvLAwAkJyfrPLSzs7MNBjuAeg6Kwg9xDaVSibS0NDg6OkrzeCxYsACAOrBRqVQA1LUZNWrUwLFjx2BnZ4eCggKpT4VCoYCPj4/BOURSU1ORkpKCxo0bG73uqKgo9OvXT9ru0aMH/v33X3h4eGgFPdevX0fjxo3Rq1cv7Ny5E/Xr10daWhrOnTuH2rVrS9eVkZGBrKwseHt765zLyckJOTk5RstDZG04MyiRFRLqEWUQQmDMmDEmvWfOnDlwc3MDoJ5Nc/bs2di1a5fUzDJ9+nQ0btxY6/X3339rNcMUVbt2bb2/4OPj4+Hj4wMXFxfY2dnpvPr27QsA6Nu3L+zs7ODi4gJ7e3vY2dnB0dER7u7uqFGjBu7fv2/w3BcvXkTv3r217kXh19q1awEAgYGBWu9zdnZGzZo1dWpWZsyYAVtbW4wfPx4AMHXqVDg6OuLVV1+VanXkcjk8PT0REBAAJycnnTIlJSXBz8/PYJmJrBFrNIisUHFNJ0Xt378fP/zwAz744AN88sknGD9+PDZs2ICRI0dKfQ9KIzg4GD///LNOup+fH3bt2qVVo1HY0aNHMX36dMybNw9PPfWU1r6CggKpRqNwH4jCYmNj8fDhQ6N9IZKTkwFAqo0wJioqCtu2bcO4ceOkmgpXV1ds2rQJnTt3xosvvogtW7bovRaN9PR0pKSkIDg4uNjzEVmVCuqESkRmsH37dgFAJCQkCCH+G61R9FV48q6+ffuKDh06SNv3798XISEhIjAwUGRkZEgjTM6dOyc2bdokhBAiJydHdOnSRZqbIz8/X3z88cfiypUrRsv377//CltbW3Hz5s0SXZehCbtMNWHCBAFAZ5bOwt5++20hl8sNTvKlcezYMeHs7CxCQ0NFZmamzqiTZcuWCQDi6aefNjgpmBBCbNy4UTRv3rxU10NUlbHphMgKiRI0nUyePBm3bt3Cpk2btEZ0NGvWTJrXYtGiRdi/fz9u3rwJQN3/YfXq1XjhhReQkZFh8Ng1a9bEc889hxUrVpT9okx0+/ZtfPnll2jevDlatWplMF9KSgr8/PyMzhOyYsUKREREwMbGBlu3boWLi4tOnnHjxmHp0qU4fvw4nnzySQwbNgx//PGHVi2QSqXCypUr8fLLL5ft4oiqIAYaRNXcF198gT179uC5557Tu//06dP4+OOP8eabb2LIkCEA1M0N33//Pf755x+jk2wBwMKFC7Fy5UppgjBLUiqVGDlyJLKzs7Fw4UKjeZOTk1GnTh2d9Pz8fKxevRotWrTA2LFj4ePjgwMHDiAsLMzgscaPH4/o6GiEhoZi06ZNeO655/D3339L+1etWoXU1FS88847pb84oqqqYitUiKgsvv/++1JNt/3ss8/qPZ5muvGvvvpKCCFEYmKiqFu3rggLCxM5OTk6+efMmSMAiEWLFhkt57Rp00RoaKjJC4tFRUUJACVaPE2lUonRo0cLAGLy5MlG8544cULI5XLx0ksv6d3/4osvCplMJoYNGybu37+vtU/fhF0aeXl54osvvhBjxoyR0qKjo4WLi4v49ddfTb4WImvCzqBEVZhmGGh0dDRq1apl0nveeecdg0Mss7OzAag7XN64cQPdu3dHQUEBdu7cqbVUusb06dOxd+9eTJ8+HZGRkahXr57e437yySe4du0aXnrpJRw6dKjYMmqG1Gr+LE5GRgbeeustbNmyBa+99po0VLawXbt2YcmSJUhNTcWVK1egVCoRGRmp93grV67E+++/jxYtWujs08wrom9+ETs7O62mqkePHuGFF17AggULTBpiTGSNGGgQVWGawEAzM6gpXF1d8eDBA737NOnZ2dnw9vZGixYt8MEHHxicdEsul2PdunV48OCBwSADUK+H8s0330h9PIpT0kAjPj4ep0+fxoQJE/DZZ5/pnYm0bdu2uHbtGh577DF07doV/fv3R7t27fQez8fHBz4+Pnr3ae655k9jPD098ccff6BZs2YmXQeRNZIJUcpxa0RElUhubq7eWhciqlgMNIiIiMhiOOqEiIiILIaBBhEREVkMAw0iIiKyGAYaREREZDEMNIiIiMhiGGgQERGRxTDQICIiIothoEFEREQWw0CDiIiILOb/AMbDMgi+mwr6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "import numpy as np\n",
    "# 根据房屋面积数据，创建NumPy数组\n",
    "x=np.array([137.97,104.50,100.00,124.32,79.20,99.00,124.00,114.00,106.69,138.05,53.75,46.91,68.00,63.02,81.26,86.21])  \n",
    "# 根据房屋价格数据，创建NumPy数组\n",
    "y=np.array([145.00,110.00,93.00,116.00,65.32,104.00,118.00,91.00,62.00,133.00,51.00,45.00,78.50,69.65,75.69,95.30])  \n",
    "# 创建画布\n",
    "plt.figure(figsize=(6,4))\n",
    "# 绘制散点图\n",
    "plt.scatter(x,y,color='red')\n",
    "# x轴标签\n",
    "plt.xlabel(\"面积(平方米)\",fontsize=14)\n",
    "# y轴标签\n",
    "plt.ylabel(\"价格(万元)\",fontsize=14)\n",
    "# x轴刻度范围\n",
    "plt.xlim(40,150)\n",
    "# y轴刻度范围\n",
    "plt.ylim(40,150)\n",
    "# 图形标题\n",
    "plt.suptitle(\"商品房销售价格评估系统\",fontsize=20)\n",
    "# 显示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f2c61db-4b69-49f9-9b22-f6d2642a056d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
